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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
21

A simulation study of the error induced in one-shine reliability confidence bounds for the Weiball distribution using a small sample size with heavily censored data /

Hartley, Michael A. January 2004 (has links) (PDF)
Thesis (M.S. in Applied Science)--Naval Postgraduate School, Dec. 2004. / Thesis Advisor(s): David H. Olwell. Includes bibliographical references (p. 57). Also available online.
22

Inference procedures based on order statistics

Frey, Jesse C., January 2005 (has links)
Thesis (Ph. D.)--Ohio State University, 2005. / Title from first page of PDF file. Document formatted into pages; contains xi, 148 p.; also includes graphics. Includes bibliographical references (p. 146-148). Available online via OhioLINK's ETD Center
23

Models for calculating confidence intervals for neural networks

Nandeshwar, Ashutosh R. January 2006 (has links)
Thesis (M.S.)--West Virginia University, 2006. / Title from document title page. Document formatted into pages; contains x, 65 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 62-65).
24

A study of nonparametric inference problems using Monte Carlo methods

Ho, Hoi-sheung. January 2005 (has links)
Thesis (Ph. D.)--University of Hong Kong, 2006. / Title proper from title frame. Also available in printed format.
25

Maximum-likelihood-based confidence regions and hypothesis tests for selected statistical models

Riggs, Kent Edward. Young, Dean M. January 2006 (has links)
Thesis (Ph.D.)--Baylor University, 2006. / Includes bibliographical references (p. 168-171).
26

A Performance Evaluation of Confidence Intervals for Ordinal Coefficient Alpha

Turner, Heather Jean 05 1900 (has links)
Ordinal coefficient alpha is a newly derived non-parametric reliability estimate. As with any point estimate, ordinal coefficient alpha is merely an estimate of a population parameter and tends to vary from sample to sample. Researchers report the confidence interval to provide readers with the amount of precision obtained. Several methods with differing computational approaches exist for confidence interval estimation for alpha, including the Fisher, Feldt, Bonner, and Hakstian and Whalen (HW) techniques. Overall, coverage rates for the various methods were unacceptably low with the Fisher method as the highest performer at 62%. Because of the poor performance across all four confidence interval methods, a need exists to develop a method which works well for ordinal coefficient alpha.
27

A Simulation Study Comparing Various Confidence Intervals for the Mean of Voucher Populations in Accounting

Lee, Ihn Shik 12 1900 (has links)
This research examined the performance of three parametric methods for confidence intervals: the classical, the Bonferroni, and the bootstrap-t method, as applied to estimating the mean of voucher populations in accounting. Usually auditing populations do not follow standard models. The population for accounting audits generally is a nonstandard mixture distribution in which the audit data set contains a large number of zero values and a comparatively small number of nonzero errors. This study assumed a situation in which only overstatement errors exist. The nonzero errors were assumed to be normally, exponentially, and uniformly distributed. Five indicators of performance were used. The classical method was found to be unreliable. The Bonferroni method was conservative for all population conditions. The bootstrap-t method was excellent in terms of reliability, but the lower limit of the confidence intervals produced by this method was unstable for all population conditions. The classical method provided the shortest average width of the confidence intervals among the three methods. This study provided initial evidence as to how the parametric bootstrap-t method performs when applied to the nonstandard distribution of audit populations of line items. Further research should provide a reliable confidence interval for a wider variety of accounting populations.
28

Some problems in model specification and inference for generalized additive models

Marra, Giampiero January 2010 (has links)
Regression models describingthe dependence between a univariate response and a set of covariates play a fundamental role in statistics. In the last two decades, a tremendous effort has been made in developing flexible regression techniques such as generalized additive models(GAMs) with the aim of modelling the expected value of a response variable as a sum of smooth unspecified functions of predictors. Many nonparametric regression methodologies exist includinglocal-weighted regressionand smoothing splines. Here the focus is on penalized regression spline methods which can be viewed as a generalization of smoothing splines with a more flexible choice of bases and penalties. This thesis addresses three issues. First, the problem of model misspecification is treated by extending the instrumental variable approach to the GAM context. Second, we study the theoretical and empirical properties of the confidence intervals for the smooth component functions of a GAM. Third, we consider the problem of variable selection within this flexible class of models. All results are supported by theoretical arguments and extensive simulation experiments which shed light on the practical performance of the methods discussed in this thesis.
29

A comparison of normal theory and bootstrap confidence intervals on the parameters of nonlinear models

Elling, Mary Margaret January 2011 (has links)
Typescript (photocopy). / Digitized by Kansas Correctional Industries
30

Estimation of the standard error and confidence interval of the indirect effect in multiple mediator models

Briggs, Nancy Elizabeth, January 2006 (has links)
Thesis (Ph. D.)--Ohio State University, 2006. / Title from first page of PDF file. Includes bibliographical references (p. 135-139).

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